Overview

Dataset statistics

Number of variables44
Number of observations20336
Missing cells222441
Missing cells (%)24.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.8 MiB
Average record size in memory352.0 B

Variable types

Numeric38
Unsupported1
Categorical5

Alerts

SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with HCO3High correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
SBP is highly correlated with MAPHigh correlation
MAP is highly correlated with SBP and 1 other fieldsHigh correlation
DBP is highly correlated with MAPHigh correlation
BaseExcess is highly correlated with HCO3 and 2 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
pH is highly correlated with BaseExcessHigh correlation
PaCO2 is highly correlated with BaseExcess and 1 other fieldsHigh correlation
BUN is highly correlated with Creatinine and 1 other fieldsHigh correlation
Creatinine is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Phosphate is highly correlated with BUN and 1 other fieldsHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
BaseExcess is highly correlated with HCO3 and 1 other fieldsHigh correlation
HCO3 is highly correlated with BaseExcessHigh correlation
pH is highly correlated with BaseExcessHigh correlation
BUN is highly correlated with CreatinineHigh correlation
Creatinine is highly correlated with BUNHigh correlation
Bilirubin_direct is highly correlated with Bilirubin_totalHigh correlation
Bilirubin_total is highly correlated with Bilirubin_directHigh correlation
Hct is highly correlated with HgbHigh correlation
Hgb is highly correlated with HctHigh correlation
Unit1 is highly correlated with Unit2High correlation
Unit2 is highly correlated with Unit1High correlation
Unit2 is highly correlated with Unit1High correlation
Unit1 is highly correlated with Unit2High correlation
Temp has 235 (1.2%) missing values Missing
SBP has 258 (1.3%) missing values Missing
DBP has 7384 (36.3%) missing values Missing
EtCO2 has 20336 (100.0%) missing values Missing
BaseExcess has 7684 (37.8%) missing values Missing
HCO3 has 535 (2.6%) missing values Missing
FiO2 has 8349 (41.1%) missing values Missing
pH has 7155 (35.2%) missing values Missing
PaCO2 has 7759 (38.2%) missing values Missing
SaO2 has 12373 (60.8%) missing values Missing
AST has 14443 (71.0%) missing values Missing
BUN has 427 (2.1%) missing values Missing
Alkalinephos has 14633 (72.0%) missing values Missing
Calcium has 3789 (18.6%) missing values Missing
Chloride has 542 (2.7%) missing values Missing
Creatinine has 461 (2.3%) missing values Missing
Bilirubin_direct has 19750 (97.1%) missing values Missing
Glucose has 407 (2.0%) missing values Missing
Lactate has 12603 (62.0%) missing values Missing
Magnesium has 1388 (6.8%) missing values Missing
Phosphate has 3650 (17.9%) missing values Missing
Potassium has 433 (2.1%) missing values Missing
Bilirubin_total has 14566 (71.6%) missing values Missing
TroponinI has 19847 (97.6%) missing values Missing
Hct has 364 (1.8%) missing values Missing
Hgb has 507 (2.5%) missing values Missing
PTT has 4496 (22.1%) missing values Missing
WBC has 625 (3.1%) missing values Missing
Fibrinogen has 17769 (87.4%) missing values Missing
Platelets has 585 (2.9%) missing values Missing
Unit1 has 9522 (46.8%) missing values Missing
Unit2 has 9522 (46.8%) missing values Missing
ICULOS is highly skewed (γ1 = 59.42175818) Skewed
PatientID has unique values Unique
EtCO2 is an unsupported type, check if it needs cleaning or further analysis Unsupported
BaseExcess has 2278 (11.2%) zeros Zeros

Reproduction

Analysis started2021-11-29 10:22:59.380060
Analysis finished2021-11-29 10:23:08.936084
Duration9.56 seconds
Software versionpandas-profiling v3.1.1
Download configurationconfig.json

Variables

PatientID
Real number (ℝ≥0)

UNIQUE

Distinct20336
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10173.60651
Minimum1
Maximum20643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:08.986696image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1017.75
Q15084.75
median10168.5
Q315252.25
95-th percentile19320.25
Maximum20643
Range20642
Interquartile range (IQR)10167.5

Descriptive statistics

Standard deviation5879.461518
Coefficient of variation (CV)0.5779132024
Kurtosis-1.192915145
Mean10173.60651
Median Absolute Deviation (MAD)5084
Skewness0.005160825078
Sum206890462
Variance34568067.75
MonotonicityStrictly increasing
2021-11-29T11:23:09.085857image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
135561
 
< 0.1%
135631
 
< 0.1%
135621
 
< 0.1%
135611
 
< 0.1%
135601
 
< 0.1%
135591
 
< 0.1%
135581
 
< 0.1%
135571
 
< 0.1%
135551
 
< 0.1%
Other values (20326)20326
> 99.9%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
101
< 0.1%
ValueCountFrequency (%)
206431
< 0.1%
206421
< 0.1%
206411
< 0.1%
206401
< 0.1%
206391
< 0.1%
206381
< 0.1%
206371
< 0.1%
206361
< 0.1%
206351
< 0.1%
206341
< 0.1%

HR
Real number (ℝ≥0)

Distinct193
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean69.85490534
Minimum20
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:09.186485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile49
Q160
median69
Q378
95-th percentile93
Maximum144
Range124
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.47874653
Coefficient of variation (CV)0.1929534722
Kurtosis0.576808424
Mean69.85490534
Median Absolute Deviation (MAD)9
Skewness0.3800833214
Sum1420499.5
Variance181.6766081
MonotonicityNot monotonic
2021-11-29T11:23:09.290969image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60731
 
3.6%
70710
 
3.5%
68604
 
3.0%
67584
 
2.9%
65559
 
2.7%
63550
 
2.7%
72548
 
2.7%
75543
 
2.7%
66542
 
2.7%
64539
 
2.7%
Other values (183)14425
70.9%
ValueCountFrequency (%)
201
 
< 0.1%
211
 
< 0.1%
222
< 0.1%
231
 
< 0.1%
241
 
< 0.1%
253
< 0.1%
261
 
< 0.1%
273
< 0.1%
27.51
 
< 0.1%
283
< 0.1%
ValueCountFrequency (%)
1441
< 0.1%
1401
< 0.1%
1371
< 0.1%
1351
< 0.1%
134.51
< 0.1%
1332
< 0.1%
1301
< 0.1%
1291
< 0.1%
1272
< 0.1%
1262
< 0.1%

O2Sat
Real number (ℝ≥0)

Distinct127
Distinct (%)0.6%
Missing12
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean91.99768746
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:09.396167image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile83
Q191
median93
Q395
95-th percentile98
Maximum100
Range80
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.722424054
Coefficient of variation (CV)0.07307166342
Kurtosis34.1866847
Mean91.99768746
Median Absolute Deviation (MAD)2
Skewness-4.824982492
Sum1869761
Variance45.19098516
MonotonicityNot monotonic
2021-11-29T11:23:09.494740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
942694
13.2%
932566
12.6%
952335
11.5%
922206
10.8%
961734
8.5%
911478
 
7.3%
971112
 
5.5%
901043
 
5.1%
98668
 
3.3%
89625
 
3.1%
Other values (117)3863
19.0%
ValueCountFrequency (%)
202
 
< 0.1%
212
 
< 0.1%
225
< 0.1%
232
 
< 0.1%
242
 
< 0.1%
253
< 0.1%
264
< 0.1%
275
< 0.1%
286
< 0.1%
293
< 0.1%
ValueCountFrequency (%)
100166
 
0.8%
99.515
 
0.1%
99317
 
1.6%
98.527
 
0.1%
98668
 
3.3%
97.541
 
0.2%
971112
5.5%
96.574
 
0.4%
961734
8.5%
95.588
 
0.4%

Temp
Real number (ℝ≥0)

MISSING

Distinct301
Distinct (%)1.5%
Missing235
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean36.06567633
Minimum20.9
Maximum39.33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:09.602324image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.9
5-th percentile35
Q135.67
median36.11
Q336.5
95-th percentile37.11
Maximum39.33
Range18.43
Interquartile range (IQR)0.83

Descriptive statistics

Standard deviation0.7491865947
Coefficient of variation (CV)0.02077284196
Kurtosis36.72512892
Mean36.06567633
Median Absolute Deviation (MAD)0.41
Skewness-2.815209632
Sum724956.16
Variance0.5612805537
MonotonicityNot monotonic
2021-11-29T11:23:09.700688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36.111168
 
5.7%
35.56850
 
4.2%
36750
 
3.7%
36.17632
 
3.1%
36.44628
 
3.1%
36.67596
 
2.9%
36.22591
 
2.9%
36.56586
 
2.9%
35.83584
 
2.9%
36.28578
 
2.8%
Other values (291)13138
64.6%
ValueCountFrequency (%)
20.91
 
< 0.1%
211
 
< 0.1%
231
 
< 0.1%
23.61
 
< 0.1%
26.61
 
< 0.1%
26.675
< 0.1%
281
 
< 0.1%
29.61
 
< 0.1%
29.611
 
< 0.1%
29.81
 
< 0.1%
ValueCountFrequency (%)
39.331
< 0.1%
39.171
< 0.1%
39.111
< 0.1%
38.891
< 0.1%
38.831
< 0.1%
38.781
< 0.1%
38.671
< 0.1%
38.612
< 0.1%
38.562
< 0.1%
38.51
< 0.1%

SBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct357
Distinct (%)1.8%
Missing258
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean95.49430421
Minimum22
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:09.803937image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile74
Q186
median93.875
Q3103.5
95-th percentile123
Maximum190
Range168
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation15.27570332
Coefficient of variation (CV)0.1599645492
Kurtosis2.112483679
Mean95.49430421
Median Absolute Deviation (MAD)8.375
Skewness0.5256272955
Sum1917334.64
Variance233.347112
MonotonicityNot monotonic
2021-11-29T11:23:09.979694image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90794
 
3.9%
91695
 
3.4%
92641
 
3.2%
93630
 
3.1%
94606
 
3.0%
95595
 
2.9%
89554
 
2.7%
88530
 
2.6%
97529
 
2.6%
87477
 
2.3%
Other values (347)14027
69.0%
ValueCountFrequency (%)
221
< 0.1%
23.51
< 0.1%
241
< 0.1%
251
< 0.1%
262
< 0.1%
271
< 0.1%
27.51
< 0.1%
282
< 0.1%
292
< 0.1%
301
< 0.1%
ValueCountFrequency (%)
1901
< 0.1%
1871
< 0.1%
1811
< 0.1%
1802
< 0.1%
178.51
< 0.1%
1761
< 0.1%
1741
< 0.1%
1721
< 0.1%
1702
< 0.1%
1691
< 0.1%

MAP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct399
Distinct (%)2.0%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean60.455898
Minimum20
Maximum125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:10.083341image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile43.67
Q154
median60
Q366.67
95-th percentile79
Maximum125
Range105
Interquartile range (IQR)12.67

Descriptive statistics

Standard deviation11.0197621
Coefficient of variation (CV)0.1822777011
Kurtosis1.542480159
Mean60.455898
Median Absolute Deviation (MAD)6
Skewness0.1463582959
Sum1229310.23
Variance121.4351568
MonotonicityNot monotonic
2021-11-29T11:23:10.181542image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60717
 
3.5%
59683
 
3.4%
61666
 
3.3%
57654
 
3.2%
58650
 
3.2%
62611
 
3.0%
63609
 
3.0%
56570
 
2.8%
64547
 
2.7%
55545
 
2.7%
Other values (389)14082
69.2%
ValueCountFrequency (%)
2019
0.1%
20.331
 
< 0.1%
20.52
 
< 0.1%
217
 
< 0.1%
21.331
 
< 0.1%
21.51
 
< 0.1%
2215
0.1%
22.53
 
< 0.1%
2312
0.1%
23.51
 
< 0.1%
ValueCountFrequency (%)
1251
< 0.1%
119.51
< 0.1%
1181
< 0.1%
1171
< 0.1%
1161
< 0.1%
115.51
< 0.1%
114.671
< 0.1%
114.331
< 0.1%
112.671
< 0.1%
111.671
< 0.1%

DBP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct172
Distinct (%)1.3%
Missing7384
Missing (%)36.3%
Infinite0
Infinite (%)0.0%
Mean47.79153799
Minimum20
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:10.276145image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile34
Q142
median47
Q353
95-th percentile65
Maximum134
Range114
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.714343907
Coefficient of variation (CV)0.2032649359
Kurtosis2.265192247
Mean47.79153799
Median Absolute Deviation (MAD)6
Skewness0.7682855006
Sum618996
Variance94.36847754
MonotonicityNot monotonic
2021-11-29T11:23:10.376867image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44598
 
2.9%
48576
 
2.8%
46575
 
2.8%
45567
 
2.8%
47565
 
2.8%
43538
 
2.6%
50531
 
2.6%
49521
 
2.6%
42488
 
2.4%
41452
 
2.2%
Other values (162)7541
37.1%
(Missing)7384
36.3%
ValueCountFrequency (%)
2012
0.1%
20.51
 
< 0.1%
215
 
< 0.1%
21.51
 
< 0.1%
2210
< 0.1%
22.51
 
< 0.1%
2319
0.1%
23.251
 
< 0.1%
2414
0.1%
24.53
 
< 0.1%
ValueCountFrequency (%)
1341
< 0.1%
1171
< 0.1%
1081
< 0.1%
1051
< 0.1%
100.51
< 0.1%
981
< 0.1%
971
< 0.1%
961
< 0.1%
952
< 0.1%
94.51
< 0.1%

Resp
Real number (ℝ≥0)

Distinct82
Distinct (%)0.4%
Missing28
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean12.03610597
Minimum1
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:10.478825image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median12
Q314
95-th percentile18
Maximum33
Range32
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.440104721
Coefficient of variation (CV)0.2858154232
Kurtosis1.296419962
Mean12.03610597
Median Absolute Deviation (MAD)2
Skewness0.4076623679
Sum244429.24
Variance11.83432049
MonotonicityNot monotonic
2021-11-29T11:23:10.575443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122942
14.5%
102611
12.8%
142085
10.3%
112029
10.0%
131785
8.8%
91375
 
6.8%
151144
 
5.6%
81076
 
5.3%
161024
 
5.0%
7595
 
2.9%
Other values (72)3642
17.9%
ValueCountFrequency (%)
119
 
0.1%
251
 
0.3%
2.51
 
< 0.1%
3100
0.5%
3.53
 
< 0.1%
3.841
 
< 0.1%
4124
0.6%
4.52
 
< 0.1%
5177
0.9%
5.251
 
< 0.1%
ValueCountFrequency (%)
331
 
< 0.1%
321
 
< 0.1%
302
 
< 0.1%
292
 
< 0.1%
28.51
 
< 0.1%
2810
< 0.1%
275
 
< 0.1%
26.51
 
< 0.1%
2616
0.1%
25.51
 
< 0.1%

EtCO2
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing20336
Missing (%)100.0%
Memory size159.0 KiB

BaseExcess
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct85
Distinct (%)0.7%
Missing7684
Missing (%)37.8%
Infinite0
Infinite (%)0.0%
Mean-2.297344293
Minimum-32
Maximum25
Zeros2278
Zeros (%)11.2%
Negative8125
Negative (%)40.0%
Memory size159.0 KiB
2021-11-29T11:23:10.675061image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-32
5-th percentile-10
Q1-4.5
median-2
Q30
95-th percentile4
Maximum25
Range57
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.570432573
Coefficient of variation (CV)-1.989441716
Kurtosis4.146076782
Mean-2.297344293
Median Absolute Deviation (MAD)2
Skewness-0.6375875951
Sum-29066
Variance20.88885391
MonotonicityNot monotonic
2021-11-29T11:23:10.767488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02278
 
11.2%
-21337
 
6.6%
-31254
 
6.2%
-11240
 
6.1%
-41009
 
5.0%
-5823
 
4.0%
-6614
 
3.0%
1586
 
2.9%
2455
 
2.2%
-7422
 
2.1%
Other values (75)2634
 
13.0%
(Missing)7684
37.8%
ValueCountFrequency (%)
-321
 
< 0.1%
-301
 
< 0.1%
-292
 
< 0.1%
-282
 
< 0.1%
-275
< 0.1%
-26.51
 
< 0.1%
-264
< 0.1%
-25.51
 
< 0.1%
-257
< 0.1%
-249
< 0.1%
ValueCountFrequency (%)
251
 
< 0.1%
231
 
< 0.1%
211
 
< 0.1%
201
 
< 0.1%
192
 
< 0.1%
183
 
< 0.1%
175
< 0.1%
1610
< 0.1%
155
< 0.1%
148
< 0.1%

HCO3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct57
Distinct (%)0.3%
Missing535
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean23.17581183
Minimum0
Maximum53
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:10.866668image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile16
Q121
median23
Q326
95-th percentile30
Maximum53
Range53
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.273794163
Coefficient of variation (CV)0.1844075277
Kurtosis2.619759907
Mean23.17581183
Median Absolute Deviation (MAD)2
Skewness0.04733510069
Sum458904.25
Variance18.26531654
MonotonicityNot monotonic
2021-11-29T11:23:10.966089image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242384
11.7%
232362
11.6%
222120
10.4%
251993
9.8%
211625
8.0%
261589
7.8%
201250
 
6.1%
271146
 
5.6%
19894
 
4.4%
28711
 
3.5%
Other values (47)3727
18.3%
ValueCountFrequency (%)
02
 
< 0.1%
510
 
< 0.1%
617
 
0.1%
79
 
< 0.1%
831
 
0.2%
931
 
0.2%
1050
0.2%
10.51
 
< 0.1%
1163
0.3%
1283
0.4%
ValueCountFrequency (%)
531
 
< 0.1%
502
 
< 0.1%
472
 
< 0.1%
462
 
< 0.1%
454
 
< 0.1%
444
 
< 0.1%
437
 
< 0.1%
429
< 0.1%
418
< 0.1%
4018
0.1%

FiO2
Real number (ℝ≥0)

MISSING

Distinct68
Distinct (%)0.6%
Missing8349
Missing (%)41.1%
Infinite0
Infinite (%)0.0%
Mean0.4424851923
Minimum0
Maximum10
Zeros64
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:11.066536image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3
Q10.4
median0.4
Q30.5
95-th percentile0.7
Maximum10
Range10
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.162033493
Coefficient of variation (CV)0.3661896393
Kurtosis1012.647919
Mean0.4424851923
Median Absolute Deviation (MAD)0.05
Skewness18.14424911
Sum5304.07
Variance0.02625485284
MonotonicityNot monotonic
2021-11-29T11:23:11.163963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.45904
29.0%
0.53221
 
15.8%
0.35904
 
4.4%
0.3368
 
1.8%
1312
 
1.5%
0.6283
 
1.4%
0.7178
 
0.9%
0.45122
 
0.6%
0.2195
 
0.5%
064
 
0.3%
Other values (58)536
 
2.6%
(Missing)8349
41.1%
ValueCountFrequency (%)
064
0.3%
0.0233
0.2%
0.0322
 
0.1%
0.0446
0.2%
0.0511
 
0.1%
0.062
 
< 0.1%
0.083
 
< 0.1%
0.13
 
< 0.1%
0.112
 
< 0.1%
0.122
 
< 0.1%
ValueCountFrequency (%)
101
 
< 0.1%
1312
1.5%
0.993
 
< 0.1%
0.986
 
< 0.1%
0.962
 
< 0.1%
0.9539
 
0.2%
0.941
 
< 0.1%
0.931
 
< 0.1%
0.921
 
< 0.1%
0.912
 
< 0.1%

pH
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct84
Distinct (%)0.6%
Missing7155
Missing (%)35.2%
Infinite0
Infinite (%)0.0%
Mean7.335895607
Minimum6.62
Maximum7.73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:11.264269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.62
5-th percentile7.2
Q17.3
median7.34
Q37.39
95-th percentile7.45
Maximum7.73
Range1.11
Interquartile range (IQR)0.09

Descriptive statistics

Standard deviation0.08094798361
Coefficient of variation (CV)0.01103450593
Kurtosis4.900062879
Mean7.335895607
Median Absolute Deviation (MAD)0.05
Skewness-1.212676517
Sum96694.44
Variance0.006552576051
MonotonicityNot monotonic
2021-11-29T11:23:11.434849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.36819
 
4.0%
7.35798
 
3.9%
7.34797
 
3.9%
7.32758
 
3.7%
7.37746
 
3.7%
7.33744
 
3.7%
7.31667
 
3.3%
7.38662
 
3.3%
7.4597
 
2.9%
7.39579
 
2.8%
Other values (74)6014
29.6%
(Missing)7155
35.2%
ValueCountFrequency (%)
6.621
 
< 0.1%
6.631
 
< 0.1%
6.651
 
< 0.1%
6.781
 
< 0.1%
6.791
 
< 0.1%
6.811
 
< 0.1%
6.823
< 0.1%
6.853
< 0.1%
6.865
< 0.1%
6.873
< 0.1%
ValueCountFrequency (%)
7.731
 
< 0.1%
7.661
 
< 0.1%
7.631
 
< 0.1%
7.591
 
< 0.1%
7.571
 
< 0.1%
7.562
 
< 0.1%
7.558
 
< 0.1%
7.546
 
< 0.1%
7.5313
0.1%
7.5222
0.1%

PaCO2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct117
Distinct (%)0.9%
Missing7759
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean36.58972728
Minimum10
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:11.533643image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile26
Q132
median36
Q340
95-th percentile49
Maximum95
Range85
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.834064984
Coefficient of variation (CV)0.2141055855
Kurtosis5.448878524
Mean36.58972728
Median Absolute Deviation (MAD)4
Skewness1.335619828
Sum460189
Variance61.37257417
MonotonicityNot monotonic
2021-11-29T11:23:11.626991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36817
 
4.0%
35785
 
3.9%
34781
 
3.8%
37759
 
3.7%
38750
 
3.7%
33711
 
3.5%
32703
 
3.5%
39605
 
3.0%
31592
 
2.9%
40589
 
2.9%
Other values (107)5485
27.0%
(Missing)7759
38.2%
ValueCountFrequency (%)
101
 
< 0.1%
113
 
< 0.1%
122
 
< 0.1%
132
 
< 0.1%
144
 
< 0.1%
156
 
< 0.1%
1610
< 0.1%
16.51
 
< 0.1%
1714
0.1%
1815
0.1%
ValueCountFrequency (%)
951
 
< 0.1%
942
< 0.1%
931
 
< 0.1%
89.51
 
< 0.1%
891
 
< 0.1%
881
 
< 0.1%
872
< 0.1%
863
< 0.1%
852
< 0.1%
842
< 0.1%

SaO2
Real number (ℝ≥0)

MISSING

Distinct116
Distinct (%)1.5%
Missing12373
Missing (%)60.8%
Infinite0
Infinite (%)0.0%
Mean84.27638453
Minimum24
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:11.725633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile54
Q171
median94
Q397
95-th percentile98
Maximum100
Range76
Interquartile range (IQR)26

Descriptive statistics

Standard deviation16.0983399
Coefficient of variation (CV)0.1910183973
Kurtosis-0.4464067556
Mean84.27638453
Median Absolute Deviation (MAD)4
Skewness-0.9075627908
Sum671092.85
Variance259.1565476
MonotonicityNot monotonic
2021-11-29T11:23:11.821024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
981307
 
6.4%
971045
 
5.1%
96689
 
3.4%
95404
 
2.0%
94319
 
1.6%
99309
 
1.5%
93195
 
1.0%
92140
 
0.7%
63128
 
0.6%
67120
 
0.6%
Other values (106)3307
 
16.3%
(Missing)12373
60.8%
ValueCountFrequency (%)
241
 
< 0.1%
261
 
< 0.1%
272
< 0.1%
281
 
< 0.1%
293
< 0.1%
304
< 0.1%
311
 
< 0.1%
322
< 0.1%
331
 
< 0.1%
33.51
 
< 0.1%
ValueCountFrequency (%)
10018
 
0.1%
99309
 
1.5%
98.52
 
< 0.1%
981307
6.4%
97.59
 
< 0.1%
971045
5.1%
96.58
 
< 0.1%
96689
3.4%
95.56
 
< 0.1%
95.41
 
< 0.1%

AST
Real number (ℝ≥0)

MISSING

Distinct653
Distinct (%)11.1%
Missing14443
Missing (%)71.0%
Infinite0
Infinite (%)0.0%
Mean134.0123027
Minimum3
Maximum9210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:11.921885image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile13
Q122
median39
Q386
95-th percentile458.8
Maximum9210
Range9207
Interquartile range (IQR)64

Descriptive statistics

Standard deviation434.3887697
Coefficient of variation (CV)3.241409638
Kurtosis136.1199603
Mean134.0123027
Median Absolute Deviation (MAD)21
Skewness10.11325823
Sum789734.5
Variance188693.6033
MonotonicityNot monotonic
2021-11-29T11:23:12.017419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17162
 
0.8%
18151
 
0.7%
19142
 
0.7%
24139
 
0.7%
16133
 
0.7%
23127
 
0.6%
15126
 
0.6%
21123
 
0.6%
20122
 
0.6%
22122
 
0.6%
Other values (643)4546
 
22.4%
(Missing)14443
71.0%
ValueCountFrequency (%)
32
 
< 0.1%
41
 
< 0.1%
52
 
< 0.1%
5.51
 
< 0.1%
66
 
< 0.1%
76
 
< 0.1%
818
 
0.1%
920
 
0.1%
1042
0.2%
1155
0.3%
ValueCountFrequency (%)
92101
< 0.1%
85911
< 0.1%
71741
< 0.1%
68841
< 0.1%
67131
< 0.1%
60001
< 0.1%
58971
< 0.1%
54351
< 0.1%
53591
< 0.1%
53311
< 0.1%

BUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct154
Distinct (%)0.8%
Missing427
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean20.25654227
Minimum1
Maximum184
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:12.114552image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q111
median15
Q323
95-th percentile54
Maximum184
Range183
Interquartile range (IQR)12

Descriptive statistics

Standard deviation16.83914067
Coefficient of variation (CV)0.8312939322
Kurtosis10.91226427
Mean20.25654227
Median Absolute Deviation (MAD)6
Skewness2.789987005
Sum403287.5
Variance283.5566586
MonotonicityNot monotonic
2021-11-29T11:23:12.215158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111143
 
5.6%
121115
 
5.5%
131106
 
5.4%
141080
 
5.3%
101076
 
5.3%
9992
 
4.9%
15965
 
4.7%
8829
 
4.1%
16821
 
4.0%
17794
 
3.9%
Other values (144)9988
49.1%
ValueCountFrequency (%)
110
 
< 0.1%
248
 
0.2%
2.51
 
< 0.1%
3142
 
0.7%
4213
 
1.0%
5385
 
1.9%
6541
2.7%
7734
3.6%
8829
4.1%
9992
4.9%
ValueCountFrequency (%)
1841
< 0.1%
1701
< 0.1%
1691
< 0.1%
1651
< 0.1%
1621
< 0.1%
1601
< 0.1%
1591
< 0.1%
1551
< 0.1%
1491
< 0.1%
1481
< 0.1%

Alkalinephos
Real number (ℝ≥0)

MISSING

Distinct455
Distinct (%)8.0%
Missing14633
Missing (%)72.0%
Infinite0
Infinite (%)0.0%
Mean104.7543398
Minimum7
Maximum3619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:12.320487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile36
Q155
median75
Q3110
95-th percentile258.9
Maximum3619
Range3612
Interquartile range (IQR)55

Descriptive statistics

Standard deviation123.7548782
Coefficient of variation (CV)1.181381873
Kurtosis184.422028
Mean104.7543398
Median Absolute Deviation (MAD)24
Skewness9.984337255
Sum597414
Variance15315.26988
MonotonicityNot monotonic
2021-11-29T11:23:12.417543image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5881
 
0.4%
4979
 
0.4%
5578
 
0.4%
5277
 
0.4%
5976
 
0.4%
7276
 
0.4%
6076
 
0.4%
5075
 
0.4%
5675
 
0.4%
6875
 
0.4%
Other values (445)4935
 
24.3%
(Missing)14633
72.0%
ValueCountFrequency (%)
71
 
< 0.1%
111
 
< 0.1%
121
 
< 0.1%
131
 
< 0.1%
142
< 0.1%
152
< 0.1%
161
 
< 0.1%
172
< 0.1%
184
< 0.1%
192
< 0.1%
ValueCountFrequency (%)
36191
< 0.1%
25281
< 0.1%
21011
< 0.1%
19191
< 0.1%
17761
< 0.1%
16691
< 0.1%
14371
< 0.1%
14361
< 0.1%
11651
< 0.1%
11551
< 0.1%

Calcium
Real number (ℝ≥0)

MISSING

Distinct92
Distinct (%)0.6%
Missing3789
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean8.144180214
Minimum1.6
Maximum15.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:12.519513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1.6
5-th percentile6.9
Q17.7
median8.2
Q38.6
95-th percentile9.3
Maximum15.7
Range14.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.7700559677
Coefficient of variation (CV)0.09455291354
Kurtosis3.578194828
Mean8.144180214
Median Absolute Deviation (MAD)0.5
Skewness-0.1429625023
Sum134761.75
Variance0.5929861933
MonotonicityNot monotonic
2021-11-29T11:23:12.614115image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.31005
 
4.9%
8.2971
 
4.8%
8.1968
 
4.8%
8.4952
 
4.7%
8.5908
 
4.5%
8885
 
4.4%
7.9854
 
4.2%
8.6852
 
4.2%
7.8779
 
3.8%
7.7763
 
3.8%
Other values (82)7610
37.4%
(Missing)3789
18.6%
ValueCountFrequency (%)
1.61
< 0.1%
2.82
< 0.1%
3.51
< 0.1%
3.61
< 0.1%
3.71
< 0.1%
3.91
< 0.1%
4.21
< 0.1%
4.32
< 0.1%
4.52
< 0.1%
4.61
< 0.1%
ValueCountFrequency (%)
15.71
< 0.1%
15.41
< 0.1%
13.61
< 0.1%
13.11
< 0.1%
12.91
< 0.1%
12.71
< 0.1%
12.42
< 0.1%
12.21
< 0.1%
11.71
< 0.1%
11.61
< 0.1%

Chloride
Real number (ℝ≥0)

MISSING

Distinct71
Distinct (%)0.4%
Missing542
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean103.8362888
Minimum26
Maximum137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:12.718390image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile95
Q1101
median104
Q3107
95-th percentile112
Maximum137
Range111
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.324223637
Coefficient of variation (CV)0.05127517268
Kurtosis5.398835036
Mean103.8362888
Median Absolute Deviation (MAD)3
Skewness-0.6879826935
Sum2055335.5
Variance28.34735734
MonotonicityNot monotonic
2021-11-29T11:23:12.886249image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1051748
 
8.6%
1041687
 
8.3%
1061644
 
8.1%
1031619
 
8.0%
1071480
 
7.3%
1021413
 
6.9%
1011212
 
6.0%
1081178
 
5.8%
1001043
 
5.1%
109997
 
4.9%
Other values (61)5773
28.4%
ValueCountFrequency (%)
261
 
< 0.1%
381
 
< 0.1%
631
 
< 0.1%
661
 
< 0.1%
701
 
< 0.1%
732
< 0.1%
741
 
< 0.1%
753
< 0.1%
763
< 0.1%
783
< 0.1%
ValueCountFrequency (%)
1371
 
< 0.1%
1331
 
< 0.1%
1321
 
< 0.1%
1311
 
< 0.1%
1302
 
< 0.1%
1291
 
< 0.1%
1282
 
< 0.1%
1272
 
< 0.1%
1252
 
< 0.1%
1246
< 0.1%

Creatinine
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct133
Distinct (%)0.7%
Missing461
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean1.184792453
Minimum0.1
Maximum25.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:12.982141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.6
median0.8
Q31.1
95-th percentile3.5
Maximum25.1
Range25
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation1.28246168
Coefficient of variation (CV)1.082435727
Kurtosis31.24324077
Mean1.184792453
Median Absolute Deviation (MAD)0.2
Skewness4.637918015
Sum23547.75
Variance1.644707961
MonotonicityNot monotonic
2021-11-29T11:23:13.081048image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72837
14.0%
0.82572
12.6%
0.62410
11.9%
0.91898
9.3%
0.51735
8.5%
11401
 
6.9%
1.1959
 
4.7%
0.4838
 
4.1%
1.2745
 
3.7%
1.3546
 
2.7%
Other values (123)3934
19.3%
(Missing)461
 
2.3%
ValueCountFrequency (%)
0.118
 
0.1%
0.268
 
0.3%
0.3255
 
1.3%
0.4838
 
4.1%
0.51735
8.5%
0.62410
11.9%
0.72837
14.0%
0.752
 
< 0.1%
0.82572
12.6%
0.91898
9.3%
ValueCountFrequency (%)
25.11
< 0.1%
17.61
< 0.1%
17.31
< 0.1%
16.51
< 0.1%
15.71
< 0.1%
15.61
< 0.1%
14.71
< 0.1%
14.21
< 0.1%
13.82
< 0.1%
13.71
< 0.1%

Bilirubin_direct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct97
Distinct (%)16.6%
Missing19750
Missing (%)97.1%
Infinite0
Infinite (%)0.0%
Mean2.391296928
Minimum0.1
Maximum37.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:13.179685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.1
Q10.4
median0.9
Q32.6
95-th percentile9.425
Maximum37.5
Range37.4
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation4.170667948
Coefficient of variation (CV)1.744102917
Kurtosis22.4578301
Mean2.391296928
Median Absolute Deviation (MAD)0.7
Skewness4.136988138
Sum1401.3
Variance17.39447114
MonotonicityNot monotonic
2021-11-29T11:23:13.277487image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.259
 
0.3%
0.144
 
0.2%
0.443
 
0.2%
0.340
 
0.2%
0.530
 
0.1%
0.628
 
0.1%
0.723
 
0.1%
0.821
 
0.1%
1.118
 
0.1%
114
 
0.1%
Other values (87)266
 
1.3%
(Missing)19750
97.1%
ValueCountFrequency (%)
0.144
0.2%
0.259
0.3%
0.340
0.2%
0.443
0.2%
0.530
0.1%
0.628
0.1%
0.723
 
0.1%
0.821
 
0.1%
0.914
 
0.1%
114
 
0.1%
ValueCountFrequency (%)
37.51
< 0.1%
351
< 0.1%
301
< 0.1%
22.81
< 0.1%
22.21
< 0.1%
21.21
< 0.1%
211
< 0.1%
19.81
< 0.1%
19.21
< 0.1%
181
< 0.1%

Glucose
Real number (ℝ≥0)

MISSING

Distinct419
Distinct (%)2.1%
Missing407
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean107.2559878
Minimum10
Maximum666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:13.379017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile64
Q187
median102
Q3121
95-th percentile165
Maximum666
Range656
Interquartile range (IQR)34

Descriptive statistics

Standard deviation35.1406194
Coefficient of variation (CV)0.3276331712
Kurtosis17.1122498
Mean107.2559878
Median Absolute Deviation (MAD)17
Skewness2.461324942
Sum2137504.58
Variance1234.863132
MonotonicityNot monotonic
2021-11-29T11:23:13.476520image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100370
 
1.8%
97344
 
1.7%
98343
 
1.7%
95334
 
1.6%
88334
 
1.6%
103328
 
1.6%
106323
 
1.6%
90318
 
1.6%
102318
 
1.6%
96318
 
1.6%
Other values (409)16599
81.6%
(Missing)407
 
2.0%
ValueCountFrequency (%)
101
 
< 0.1%
111
 
< 0.1%
141
 
< 0.1%
171
 
< 0.1%
181
 
< 0.1%
192
< 0.1%
212
< 0.1%
221
 
< 0.1%
242
< 0.1%
253
< 0.1%
ValueCountFrequency (%)
6661
< 0.1%
6511
< 0.1%
5631
< 0.1%
5011
< 0.1%
4721
< 0.1%
4342
< 0.1%
4201
< 0.1%
4181
< 0.1%
4152
< 0.1%
4071
< 0.1%

Lactate
Real number (ℝ≥0)

MISSING

Distinct171
Distinct (%)2.2%
Missing12603
Missing (%)62.0%
Infinite0
Infinite (%)0.0%
Mean1.679900427
Minimum0.2
Maximum26.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:13.579952image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.7
Q11
median1.3
Q31.9
95-th percentile3.57
Maximum26.9
Range26.7
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation1.385789896
Coefficient of variation (CV)0.8249238309
Kurtosis55.27437723
Mean1.679900427
Median Absolute Deviation (MAD)0.4
Skewness5.854482183
Sum12990.67
Variance1.920413635
MonotonicityNot monotonic
2021-11-29T11:23:13.680412image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1620
 
3.0%
0.9588
 
2.9%
1.2573
 
2.8%
1.1545
 
2.7%
1.3508
 
2.5%
0.8482
 
2.4%
1.4471
 
2.3%
1.6393
 
1.9%
1.5380
 
1.9%
1.7302
 
1.5%
Other values (161)2871
 
14.1%
(Missing)12603
62.0%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.35
 
< 0.1%
0.371
 
< 0.1%
0.418
 
0.1%
0.550
 
0.2%
0.551
 
< 0.1%
0.6166
0.8%
0.7299
1.5%
0.731
 
< 0.1%
0.751
 
< 0.1%
ValueCountFrequency (%)
26.91
< 0.1%
22.41
< 0.1%
17.81
< 0.1%
17.51
< 0.1%
17.41
< 0.1%
16.751
< 0.1%
16.71
< 0.1%
16.42
< 0.1%
15.42
< 0.1%
15.31
< 0.1%

Magnesium
Real number (ℝ≥0)

MISSING

Distinct47
Distinct (%)0.2%
Missing1388
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean1.864042643
Minimum0.2
Maximum8.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:13.778253image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.4
Q11.7
median1.8
Q32
95-th percentile2.4
Maximum8.2
Range8
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.3432759491
Coefficient of variation (CV)0.1841567039
Kurtosis12.96717616
Mean1.864042643
Median Absolute Deviation (MAD)0.2
Skewness1.286745587
Sum35319.88
Variance0.1178383772
MonotonicityNot monotonic
2021-11-29T11:23:13.873482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
1.82691
13.2%
1.92565
12.6%
1.72362
11.6%
22041
10.0%
1.61757
8.6%
2.11539
7.6%
1.51189
5.8%
2.21075
 
5.3%
1.4728
 
3.6%
2.3718
 
3.5%
Other values (37)2283
11.2%
(Missing)1388
6.8%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.75
 
< 0.1%
0.87
 
< 0.1%
0.925
 
0.1%
178
 
0.4%
1.1129
 
0.6%
1.141
 
< 0.1%
1.2249
 
1.2%
1.3430
2.1%
1.4728
3.6%
ValueCountFrequency (%)
8.21
 
< 0.1%
6.52
 
< 0.1%
6.21
 
< 0.1%
4.61
 
< 0.1%
4.51
 
< 0.1%
4.21
 
< 0.1%
4.12
 
< 0.1%
43
< 0.1%
3.81
 
< 0.1%
3.75
< 0.1%

Phosphate
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct114
Distinct (%)0.7%
Missing3650
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean3.207590195
Minimum0.2
Maximum13.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:13.974112image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile1.6
Q12.4
median3.1
Q33.8
95-th percentile5.3
Maximum13.5
Range13.3
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation1.19385278
Coefficient of variation (CV)0.3721961681
Kurtosis4.851664704
Mean3.207590195
Median Absolute Deviation (MAD)0.7
Skewness1.38697156
Sum53521.85
Variance1.425284459
MonotonicityNot monotonic
2021-11-29T11:23:14.081889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.9714
 
3.5%
3.1712
 
3.5%
2.7708
 
3.5%
3.2688
 
3.4%
2.8678
 
3.3%
2.6655
 
3.2%
3.3623
 
3.1%
3619
 
3.0%
2.5605
 
3.0%
3.4579
 
2.8%
Other values (104)10105
49.7%
(Missing)3650
 
17.9%
ValueCountFrequency (%)
0.21
 
< 0.1%
0.35
 
< 0.1%
0.43
 
< 0.1%
0.511
 
0.1%
0.613
 
0.1%
0.724
 
0.1%
0.827
 
0.1%
0.923
 
0.1%
151
0.3%
1.175
0.4%
ValueCountFrequency (%)
13.51
 
< 0.1%
12.91
 
< 0.1%
12.41
 
< 0.1%
12.31
 
< 0.1%
12.22
< 0.1%
12.11
 
< 0.1%
11.21
 
< 0.1%
11.11
 
< 0.1%
113
< 0.1%
10.53
< 0.1%

Potassium
Real number (ℝ≥0)

MISSING

Distinct71
Distinct (%)0.4%
Missing433
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean3.815108275
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:14.186030image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13.5
median3.8
Q34.1
95-th percentile4.7
Maximum9
Range8
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.516029457
Coefficient of variation (CV)0.1352594526
Kurtosis1.755362517
Mean3.815108275
Median Absolute Deviation (MAD)0.3
Skewness0.4984364654
Sum75932.1
Variance0.2662864004
MonotonicityNot monotonic
2021-11-29T11:23:14.360883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.81780
 
8.8%
3.71743
 
8.6%
3.61665
 
8.2%
3.91581
 
7.8%
3.51427
 
7.0%
41376
 
6.8%
3.41258
 
6.2%
4.11204
 
5.9%
4.21019
 
5.0%
3.3966
 
4.8%
Other values (61)5884
28.9%
ValueCountFrequency (%)
11
 
< 0.1%
1.51
 
< 0.1%
1.61
 
< 0.1%
1.82
 
< 0.1%
1.95
 
< 0.1%
24
 
< 0.1%
2.110
 
< 0.1%
2.26
 
< 0.1%
2.314
0.1%
2.426
0.1%
ValueCountFrequency (%)
91
 
< 0.1%
7.11
 
< 0.1%
6.91
 
< 0.1%
6.71
 
< 0.1%
6.53
< 0.1%
6.42
 
< 0.1%
6.32
 
< 0.1%
6.26
< 0.1%
6.12
 
< 0.1%
66
< 0.1%

Bilirubin_total
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct206
Distinct (%)3.6%
Missing14566
Missing (%)71.6%
Infinite0
Infinite (%)0.0%
Mean1.586646447
Minimum0.1
Maximum45.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:14.463584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.2
95-th percentile5.6
Maximum45.9
Range45.8
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation3.545279592
Coefficient of variation (CV)2.234448385
Kurtosis51.58048869
Mean1.586646447
Median Absolute Deviation (MAD)0.3
Skewness6.412716124
Sum9154.95
Variance12.56900739
MonotonicityNot monotonic
2021-11-29T11:23:14.561925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3631
 
3.1%
0.4618
 
3.0%
0.5596
 
2.9%
0.6488
 
2.4%
0.7417
 
2.1%
0.2394
 
1.9%
0.8315
 
1.5%
0.9273
 
1.3%
1199
 
1.0%
1.1172
 
0.8%
Other values (196)1667
 
8.2%
(Missing)14566
71.6%
ValueCountFrequency (%)
0.188
 
0.4%
0.2394
1.9%
0.3631
3.1%
0.4618
3.0%
0.451
 
< 0.1%
0.5596
2.9%
0.6488
2.4%
0.7417
2.1%
0.8315
1.5%
0.9273
1.3%
ValueCountFrequency (%)
45.91
< 0.1%
44.91
< 0.1%
44.11
< 0.1%
43.51
< 0.1%
43.21
< 0.1%
40.61
< 0.1%
40.12
< 0.1%
38.71
< 0.1%
36.31
< 0.1%
34.71
< 0.1%

TroponinI
Real number (ℝ≥0)

MISSING

Distinct182
Distinct (%)37.2%
Missing19847
Missing (%)97.6%
Infinite0
Infinite (%)0.0%
Mean7.40204499
Minimum0.3
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:14.659996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.7
median2.4
Q39.9
95-th percentile32.12
Maximum48
Range47.7
Interquartile range (IQR)9.2

Descriptive statistics

Standard deviation10.40468427
Coefficient of variation (CV)1.405649964
Kurtosis3.102736102
Mean7.40204499
Median Absolute Deviation (MAD)2
Skewness1.917343008
Sum3619.6
Variance108.2574548
MonotonicityNot monotonic
2021-11-29T11:23:14.762874image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.344
 
0.2%
0.430
 
0.1%
0.525
 
0.1%
0.823
 
0.1%
0.621
 
0.1%
0.718
 
0.1%
115
 
0.1%
0.911
 
0.1%
1.27
 
< 0.1%
10.77
 
< 0.1%
Other values (172)288
 
1.4%
(Missing)19847
97.6%
ValueCountFrequency (%)
0.344
0.2%
0.430
0.1%
0.525
0.1%
0.621
0.1%
0.718
0.1%
0.823
0.1%
0.911
 
0.1%
115
 
0.1%
1.16
 
< 0.1%
1.27
 
< 0.1%
ValueCountFrequency (%)
481
< 0.1%
46.51
< 0.1%
452
< 0.1%
44.81
< 0.1%
44.21
< 0.1%
43.51
< 0.1%
42.91
< 0.1%
41.51
< 0.1%
411
< 0.1%
39.91
< 0.1%

Hct
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct485
Distinct (%)2.4%
Missing364
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean29.55105848
Minimum5.5
Maximum66.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:14.864097image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5.5
5-th percentile21.8
Q125.7
median29
Q333
95-th percentile39.1
Maximum66.2
Range60.7
Interquartile range (IQR)7.3

Descriptive statistics

Standard deviation5.415361492
Coefficient of variation (CV)0.1832544
Kurtosis0.4366050879
Mean29.55105848
Median Absolute Deviation (MAD)3.6
Skewness0.4137097912
Sum590193.74
Variance29.32614009
MonotonicityNot monotonic
2021-11-29T11:23:14.958620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26285
 
1.4%
28228
 
1.1%
27226
 
1.1%
25219
 
1.1%
29199
 
1.0%
30199
 
1.0%
24191
 
0.9%
23190
 
0.9%
26.5161
 
0.8%
27.8161
 
0.8%
Other values (475)17913
88.1%
(Missing)364
 
1.8%
ValueCountFrequency (%)
5.51
< 0.1%
71
< 0.1%
8.81
< 0.1%
9.41
< 0.1%
9.71
< 0.1%
10.31
< 0.1%
111
< 0.1%
11.51
< 0.1%
12.11
< 0.1%
12.21
< 0.1%
ValueCountFrequency (%)
66.21
< 0.1%
61.71
< 0.1%
60.32
< 0.1%
56.11
< 0.1%
55.31
< 0.1%
54.81
< 0.1%
54.11
< 0.1%
541
< 0.1%
53.82
< 0.1%
531
< 0.1%

Hgb
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct205
Distinct (%)1.0%
Missing507
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean10.16811286
Minimum2.2
Maximum20.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:15.061164image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile7.5
Q18.9
median10
Q311.3
95-th percentile13.5
Maximum20.3
Range18.1
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation1.844816211
Coefficient of variation (CV)0.1814315238
Kurtosis0.2644858416
Mean10.16811286
Median Absolute Deviation (MAD)1.2
Skewness0.3964761228
Sum201623.51
Variance3.403346852
MonotonicityNot monotonic
2021-11-29T11:23:15.160439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.8481
 
2.4%
9.4469
 
2.3%
9.7464
 
2.3%
9.2460
 
2.3%
9.5450
 
2.2%
10440
 
2.2%
9.6426
 
2.1%
9422
 
2.1%
9.3421
 
2.1%
9.9421
 
2.1%
Other values (195)15375
75.6%
(Missing)507
 
2.5%
ValueCountFrequency (%)
2.21
 
< 0.1%
3.11
 
< 0.1%
3.22
< 0.1%
41
 
< 0.1%
4.051
 
< 0.1%
4.12
< 0.1%
4.22
< 0.1%
4.33
< 0.1%
4.41
 
< 0.1%
4.52
< 0.1%
ValueCountFrequency (%)
20.31
 
< 0.1%
19.32
< 0.1%
19.11
 
< 0.1%
18.61
 
< 0.1%
18.41
 
< 0.1%
182
< 0.1%
17.41
 
< 0.1%
17.13
< 0.1%
171
 
< 0.1%
16.92
< 0.1%

PTT
Real number (ℝ≥0)

MISSING

Distinct688
Distinct (%)4.3%
Missing4496
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean31.89517109
Minimum12.5
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:15.265334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12.5
5-th percentile22.3
Q125.9
median29.1
Q333.9
95-th percentile50.7
Maximum150
Range137.5
Interquartile range (IQR)8

Descriptive statistics

Standard deviation11.61275924
Coefficient of variation (CV)0.3640914548
Kurtosis32.60193918
Mean31.89517109
Median Absolute Deviation (MAD)3.7
Skewness4.498845459
Sum505219.51
Variance134.8561772
MonotonicityNot monotonic
2021-11-29T11:23:15.360709image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27.7155
 
0.8%
27.6145
 
0.7%
28.1140
 
0.7%
27136
 
0.7%
28.5135
 
0.7%
26.7133
 
0.7%
28.6133
 
0.7%
26.2133
 
0.7%
28.7132
 
0.6%
26.6131
 
0.6%
Other values (678)14467
71.1%
(Missing)4496
 
22.1%
ValueCountFrequency (%)
12.51
 
< 0.1%
16.61
 
< 0.1%
17.13
< 0.1%
17.21
 
< 0.1%
17.31
 
< 0.1%
17.41
 
< 0.1%
17.51
 
< 0.1%
17.92
< 0.1%
18.13
< 0.1%
18.23
< 0.1%
ValueCountFrequency (%)
15034
0.2%
145.91
 
< 0.1%
143.71
 
< 0.1%
142.11
 
< 0.1%
1391
 
< 0.1%
137.81
 
< 0.1%
135.31
 
< 0.1%
1311
 
< 0.1%
127.91
 
< 0.1%
127.21
 
< 0.1%

WBC
Real number (ℝ≥0)

MISSING

Distinct429
Distinct (%)2.2%
Missing625
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean10.40438385
Minimum0.1
Maximum201.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:15.466207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile4.3
Q17.2
median9.6
Q312.5
95-th percentile18.5
Maximum201.6
Range201.5
Interquartile range (IQR)5.3

Descriptive statistics

Standard deviation5.88137598
Coefficient of variation (CV)0.5652786428
Kurtosis174.8237677
Mean10.40438385
Median Absolute Deviation (MAD)2.6
Skewness8.145523781
Sum205080.81
Variance34.59058341
MonotonicityNot monotonic
2021-11-29T11:23:15.567376image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.5229
 
1.1%
9.4225
 
1.1%
8219
 
1.1%
9.3217
 
1.1%
7.2216
 
1.1%
8.9215
 
1.1%
7.7214
 
1.1%
8.8213
 
1.0%
8.4213
 
1.0%
7.4213
 
1.0%
Other values (419)17537
86.2%
(Missing)625
 
3.1%
ValueCountFrequency (%)
0.114
0.1%
0.210
< 0.1%
0.36
< 0.1%
0.46
< 0.1%
0.52
 
< 0.1%
0.64
 
< 0.1%
0.74
 
< 0.1%
0.81
 
< 0.1%
0.95
 
< 0.1%
18
< 0.1%
ValueCountFrequency (%)
201.61
< 0.1%
180.41
< 0.1%
168.61
< 0.1%
128.31
< 0.1%
126.21
< 0.1%
124.41
< 0.1%
120.51
< 0.1%
119.91
< 0.1%
116.51
< 0.1%
97.11
< 0.1%

Fibrinogen
Real number (ℝ≥0)

MISSING

Distinct644
Distinct (%)25.1%
Missing17769
Missing (%)87.4%
Infinite0
Infinite (%)0.0%
Mean292.9410206
Minimum34
Maximum1383
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:15.744858image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum34
5-th percentile105.3
Q1176
median242
Q3367
95-th percentile648
Maximum1383
Range1349
Interquartile range (IQR)191

Descriptive statistics

Standard deviation169.5769129
Coefficient of variation (CV)0.5788773199
Kurtosis2.436807156
Mean292.9410206
Median Absolute Deviation (MAD)85
Skewness1.427978388
Sum751979.6
Variance28756.3294
MonotonicityNot monotonic
2021-11-29T11:23:15.849360image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21719
 
0.1%
21418
 
0.1%
15117
 
0.1%
18517
 
0.1%
20215
 
0.1%
18315
 
0.1%
21014
 
0.1%
20314
 
0.1%
24214
 
0.1%
18014
 
0.1%
Other values (634)2410
 
11.9%
(Missing)17769
87.4%
ValueCountFrequency (%)
341
 
< 0.1%
351
 
< 0.1%
501
 
< 0.1%
521
 
< 0.1%
52.51
 
< 0.1%
561
 
< 0.1%
581
 
< 0.1%
595
< 0.1%
601
 
< 0.1%
611
 
< 0.1%
ValueCountFrequency (%)
13831
< 0.1%
12461
< 0.1%
11611
< 0.1%
10301
< 0.1%
9761
< 0.1%
9601
< 0.1%
9561
< 0.1%
9461
< 0.1%
9101
< 0.1%
9041
< 0.1%

Platelets
Real number (ℝ≥0)

MISSING

Distinct704
Distinct (%)3.6%
Missing585
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean195.8909675
Minimum5
Maximum1592
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:15.951641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile68
Q1129
median178
Q3241
95-th percentile379
Maximum1592
Range1587
Interquartile range (IQR)112

Descriptive statistics

Standard deviation103.0525545
Coefficient of variation (CV)0.5260709862
Kurtosis9.396094818
Mean195.8909675
Median Absolute Deviation (MAD)55
Skewness1.94562446
Sum3869042.5
Variance10619.82898
MonotonicityNot monotonic
2021-11-29T11:23:16.050098image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
160133
 
0.7%
195122
 
0.6%
172119
 
0.6%
179119
 
0.6%
158112
 
0.6%
170112
 
0.6%
156110
 
0.5%
125110
 
0.5%
188109
 
0.5%
157109
 
0.5%
Other values (694)18596
91.4%
(Missing)585
 
2.9%
ValueCountFrequency (%)
55
< 0.1%
62
 
< 0.1%
75
< 0.1%
81
 
< 0.1%
93
< 0.1%
103
< 0.1%
113
< 0.1%
127
< 0.1%
135
< 0.1%
144
< 0.1%
ValueCountFrequency (%)
15921
< 0.1%
14211
< 0.1%
13431
< 0.1%
12621
< 0.1%
11421
< 0.1%
10241
< 0.1%
10071
< 0.1%
9921
< 0.1%
9841
< 0.1%
9611
< 0.1%

Age
Real number (ℝ≥0)

Distinct5971
Distinct (%)29.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.6216129
Minimum18.11
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:16.155946image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum18.11
5-th percentile30.8475
Q152.29
median64.67
Q375.66
95-th percentile84.95
Maximum89
Range70.89
Interquartile range (IQR)23.37

Descriptive statistics

Standard deviation16.23615352
Coefficient of variation (CV)0.2592739594
Kurtosis-0.2520549698
Mean62.6216129
Median Absolute Deviation (MAD)11.61
Skewness-0.5902903968
Sum1273473.12
Variance263.6126812
MonotonicityNot monotonic
2021-11-29T11:23:16.254699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.8213
 
0.1%
69.6812
 
0.1%
61.0812
 
0.1%
68.1712
 
0.1%
65.4712
 
0.1%
71.3712
 
0.1%
68.3711
 
0.1%
69.5811
 
0.1%
78.4211
 
0.1%
60.8811
 
0.1%
Other values (5961)20219
99.4%
ValueCountFrequency (%)
18.113
< 0.1%
18.131
 
< 0.1%
18.142
< 0.1%
18.151
 
< 0.1%
18.181
 
< 0.1%
18.241
 
< 0.1%
18.321
 
< 0.1%
18.341
 
< 0.1%
18.352
< 0.1%
18.361
 
< 0.1%
ValueCountFrequency (%)
891
 
< 0.1%
88.991
 
< 0.1%
88.982
 
< 0.1%
88.974
< 0.1%
88.961
 
< 0.1%
88.954
< 0.1%
88.942
 
< 0.1%
88.931
 
< 0.1%
88.925
< 0.1%
88.94
< 0.1%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
1
11834 
0
8502 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Length

2021-11-29T11:23:16.352994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:16.406027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring characters

ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
111834
58.2%
08502
41.8%

Unit1
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
0.0
5470 
1.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.05470
26.9%
1.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:23:16.457665image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:16.506915image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0.05470
50.6%
1.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016284
75.3%
15344
 
24.7%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016284
50.2%
.10814
33.3%
15344
 
16.5%

Unit2
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing9522
Missing (%)46.8%
Memory size159.0 KiB
1.0
5470 
0.0
5344 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters32442
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.05470
26.9%
0.05344
26.3%
(Missing)9522
46.8%

Length

2021-11-29T11:23:16.558954image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:16.607984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1.05470
50.6%
0.05344
49.4%

Most occurring characters

ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number21628
66.7%
Other Punctuation10814
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
016158
74.7%
15470
 
25.3%
Other Punctuation
ValueCountFrequency (%)
.10814
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common32442
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII32442
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
016158
49.8%
.10814
33.3%
15470
 
16.9%

HospAdmTime
Real number (ℝ)

Distinct7152
Distinct (%)35.2%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-48.67841062
Minimum-3710.66
Maximum23.99
Zeros168
Zeros (%)0.8%
Negative19912
Negative (%)97.9%
Memory size159.0 KiB
2021-11-29T11:23:16.670767image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-3710.66
5-th percentile-236.12
Q1-34.135
median-2.77
Q3-0.02
95-th percentile-0.01
Maximum23.99
Range3734.65
Interquartile range (IQR)34.115

Descriptive statistics

Standard deviation143.6833182
Coefficient of variation (CV)-2.951684666
Kurtosis123.0885825
Mean-48.67841062
Median Absolute Deviation (MAD)2.75
Skewness-8.542504055
Sum-989875.48
Variance20644.89593
MonotonicityNot monotonic
2021-11-29T11:23:16.770859image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.023749
 
18.4%
-0.032290
 
11.3%
-0.011114
 
5.5%
-0.04658
 
3.2%
-0.05314
 
1.5%
0168
 
0.8%
-0.06136
 
0.7%
-0.0782
 
0.4%
-0.0840
 
0.2%
-0.0932
 
0.2%
Other values (7142)11752
57.8%
ValueCountFrequency (%)
-3710.661
< 0.1%
-3322.91
< 0.1%
-3269.11
< 0.1%
-3212.561
< 0.1%
-3141.551
< 0.1%
-2668.771
< 0.1%
-2562.531
< 0.1%
-2506.691
< 0.1%
-2476.581
< 0.1%
-2379.761
< 0.1%
ValueCountFrequency (%)
23.991
< 0.1%
22.041
< 0.1%
20.041
< 0.1%
17.341
< 0.1%
16.021
< 0.1%
14.651
< 0.1%
14.211
< 0.1%
141
< 0.1%
11.941
< 0.1%
10.991
< 0.1%

ICULOS
Real number (ℝ≥0)

SKEWED

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.916256884
Minimum1
Maximum304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:16.870584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum304
Range303
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.852074093
Coefficient of variation (CV)2.010207569
Kurtosis4370.912417
Mean1.916256884
Median Absolute Deviation (MAD)0
Skewness59.42175818
Sum38969
Variance14.83847482
MonotonicityNot monotonic
2021-11-29T11:23:16.956768image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
112839
63.1%
23388
 
16.7%
31678
 
8.3%
41003
 
4.9%
5576
 
2.8%
6369
 
1.8%
7208
 
1.0%
8109
 
0.5%
960
 
0.3%
1029
 
0.1%
Other values (21)77
 
0.4%
ValueCountFrequency (%)
112839
63.1%
23388
 
16.7%
31678
 
8.3%
41003
 
4.9%
5576
 
2.8%
6369
 
1.8%
7208
 
1.0%
8109
 
0.5%
960
 
0.3%
1029
 
0.1%
ValueCountFrequency (%)
3041
 
< 0.1%
2821
 
< 0.1%
2691
 
< 0.1%
304
< 0.1%
281
 
< 0.1%
272
< 0.1%
262
< 0.1%
251
 
< 0.1%
241
 
< 0.1%
234
< 0.1%

SepsisLabel
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
0
20133 
1
 
203

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Length

2021-11-29T11:23:17.047973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:17.175555image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Most occurring characters

ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
020133
99.0%
1203
 
1.0%

Sepsis
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size159.0 KiB
0
18546 
1
 
1790

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters20336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Length

2021-11-29T11:23:17.230314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-11-29T11:23:17.283321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring characters

ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring scripts

ValueCountFrequency (%)
Common20336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII20336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
018546
91.2%
11790
 
8.8%

Hours
Real number (ℝ≥0)

Distinct228
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.85793666
Minimum8
Maximum336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size159.0 KiB
2021-11-29T11:23:17.345411image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile15
Q125
median39
Q347
95-th percentile58
Maximum336
Range328
Interquartile range (IQR)22

Descriptive statistics

Standard deviation22.30865867
Coefficient of variation (CV)0.5741081639
Kurtosis40.49091179
Mean38.85793666
Median Absolute Deviation (MAD)11
Skewness4.703211094
Sum790215
Variance497.6762517
MonotonicityNot monotonic
2021-11-29T11:23:17.445210image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36700
 
3.4%
39665
 
3.3%
38656
 
3.2%
40634
 
3.1%
41632
 
3.1%
37632
 
3.1%
43600
 
3.0%
42597
 
2.9%
44580
 
2.9%
46560
 
2.8%
Other values (218)14080
69.2%
ValueCountFrequency (%)
8124
 
0.6%
9122
 
0.6%
1095
 
0.5%
11114
 
0.6%
12121
 
0.6%
13145
0.7%
14194
1.0%
15231
1.1%
16274
1.3%
17348
1.7%
ValueCountFrequency (%)
3365
< 0.1%
3351
 
< 0.1%
3341
 
< 0.1%
3301
 
< 0.1%
3281
 
< 0.1%
3052
 
< 0.1%
2971
 
< 0.1%
2861
 
< 0.1%
2791
 
< 0.1%
2771
 
< 0.1%

Interactions

2021-11-29T11:23:05.990271image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.528126image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.617165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.706683image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.800610image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.897077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:02.986880image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.074336image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.162053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.321189image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.409576image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.495744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.581321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.668582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.751183image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.841330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:03.927514image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.017149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.111708image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.203538image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.292756image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.378506image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.469599image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.558995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.646561image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.738504image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.829661image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:04.920716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.005814image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.090108image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.177204image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.342533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.432614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.524286image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.617865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.714040image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.805496image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-29T11:23:05.900400image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-29T11:23:17.590797image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-29T11:23:17.937712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-29T11:23:18.282545image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-29T11:23:18.571729image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-11-29T11:23:06.232059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-29T11:23:07.305787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-11-29T11:23:08.017265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-11-29T11:23:08.755712image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
0176.085.036.1178.044.00NaN17.0NaN18.045.00.257.3186.078.016.014.098.09.385.00.7NaN133.0NaN2.03.33.80.3NaN36.212.2NaN5.7NaN317.083.140NaNNaN-0.0310054
1254.094.036.00114.050.5036.09.0NaNNaN22.0NaNNaNNaNNaNNaN100.0NaN7.9113.02.5NaN78.0NaN2.54.45.1NaNNaN27.89.7NaN11.0NaN158.075.9100.01.0-98.6010023
2368.091.036.89122.062.6744.017.0NaN5.029.00.507.4938.0NaNNaN25.0NaN10.998.00.8NaN51.0NaN2.42.33.4NaNNaN26.28.829.58.3NaN465.045.8201.00.0-1195.7110048
3493.095.536.0690.034.0044.014.0NaN0.022.0NaN7.3641.097.5NaN14.0NaN8.2105.00.8NaN69.0NaN1.73.84.0NaNNaN24.08.321.37.6NaN144.065.7100.01.0-8.7710029
4561.096.036.22114.073.00NaN14.0NaNNaN24.0NaNNaNNaNNaN16.06.062.07.8105.00.6NaN103.0NaN1.92.83.10.5NaN39.714.229.04.7NaN273.028.0911.00.0-0.0520048
5687.095.036.33101.073.00NaN18.5NaN0.029.00.407.3447.0NaNNaN9.0NaNNaN111.00.7NaN68.01.4NaNNaN3.8NaNNaN36.912.2NaN12.0NaN298.052.0111.00.0-0.0330017
67103.093.037.2891.059.0045.012.0NaN-12.013.00.407.2223.0NaN452.052.088.05.9111.03.5NaN71.02.21.60.92.81.4NaN36.714.525.47.2NaN26.064.2411.00.0-0.0510045
7865.079.035.6789.057.0040.012.0NaN-11.015.0NaN7.2722.0NaNNaN27.0NaN7.4105.01.1NaN84.00.81.82.73.2NaNNaN25.08.6NaN9.0NaN205.087.081NaNNaN-2.2310040
8985.089.535.3378.056.0044.013.0NaN-7.023.00.357.1332.074.0NaN11.0NaN7.4103.00.7NaN87.00.81.11.73.0NaNNaN21.87.324.23.9124.064.027.921NaNNaN-0.03101258
91063.090.035.5097.063.0047.010.0NaN-3.023.00.407.3237.096.0NaN17.0NaNNaN105.00.9NaN92.01.12.1NaN3.7NaNNaN27.99.529.98.7NaN107.076.7100.01.0-2.3630023

Last rows

PatientIDHRO2SatTempSBPMAPDBPRespEtCO2BaseExcessHCO3FiO2pHPaCO2SaO2ASTBUNAlkalinephosCalciumChlorideCreatinineBilirubin_directGlucoseLactateMagnesiumPhosphatePotassiumBilirubin_totalTroponinIHctHgbPTTWBCFibrinogenPlateletsAgeGenderUnit1Unit2HospAdmTimeICULOSSepsisLabelSepsisHours
203262063470.094.034.6089.060.044.010.0NaN0.027.00.47.3439.075.0NaN15.0NaNNaN111.00.6NaN70.0NaN2.2NaN3.8NaNNaN25.59.426.48.3NaN223.057.2610.01.0-2.9030020
203272063553.092.036.33112.063.053.010.0NaN0.024.0NaN7.4137.0NaNNaN12.0NaN7.8110.00.8NaN96.01.01.82.23.7NaNNaN28.710.134.59.0NaN301.056.751NaNNaN-0.0120042
203282063660.094.035.6188.556.037.09.0NaN0.022.00.47.3341.074.01033.032.0204.0NaN102.01.2NaN76.0NaN1.5NaN4.41.8NaN27.39.538.34.6NaN183.082.3600.01.00.3250043
203292063761.085.035.0090.046.054.07.0NaN-9.013.00.37.2336.0NaNNaN44.0NaN7.286.03.5NaN70.00.61.44.64.1NaNNaN26.38.635.54.3NaN170.060.6611.00.0-0.02201142
203302063865.092.035.78113.075.056.014.0NaNNaN26.0NaNNaNNaNNaNNaN17.0NaN9.1102.01.3NaN98.0NaN2.03.53.6NaNNaN37.713.024.86.5NaN176.068.381NaNNaN-0.0220041
203312063974.097.035.7290.058.5NaN16.0NaNNaN19.0NaNNaNNaNNaN80.032.0154.08.2105.00.8NaN89.0NaN1.64.04.13.1NaN27.49.733.418.1263.012.059.1411.00.0-0.0210026
203322064071.093.036.10107.565.549.08.0NaN-7.020.00.57.2635.095.0NaN15.0NaN7.7106.00.7NaN101.0NaN1.9NaN3.6NaNNaN26.99.735.414.3NaN134.074.5300.01.0-59.0930025
203332064176.094.036.6793.050.045.012.0NaNNaN30.0NaNNaNNaNNaNNaN16.0NaN8.796.00.7NaN101.0NaN2.23.33.4NaNNaN32.411.123.08.1NaN291.033.011NaNNaN-0.0160021
203342064279.096.035.6783.058.043.011.0NaN0.026.00.47.4534.099.0NaN12.0NaN8.8105.00.4NaN117.0NaN1.93.93.7NaNNaN30.810.327.47.3NaN247.069.800NaNNaN-10.5810042
203352064379.088.036.06108.042.561.016.0NaN-6.025.00.47.1634.085.090.015.0107.08.1102.01.5NaN107.01.61.63.53.50.5NaN27.810.626.111.8NaN284.062.291NaNNaN-0.0330133